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Using Big Data To Improve Consumer Sentiment

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The aggregate amount of data available to retailers is doubling every year, and picking out useful information from such a large pool can be a tremendous challenge. However, the insights into customers’ actions afforded by Big Data are now invaluable to retail marketers, struggling as they are to cope with increased competition and ever-declining customer loyalty levels.

One of the most important ways it allows retail marketers to improve customer sentiment is by allowing them to target promotions and offers in real time. Customers can be segmented, and material sent to them based on factors such as purchasing habits and location, and then on temporal factors as and when they occur, such as the weather. One pizza chain, for example, has seen a 20% response rate from coupons it sends when bad weather or power outages strike. The data is not just being utilized online either, but it is also being leveraged in physical stores.

If a retailer had wanted to gain a full understanding of their customers and how they bought goods when brick and mortar shops were the sole purchasing channel, they’d have to follow them around with a pen and paper making notes. Which was, if anything, off-putting. Thanks to e-commerce, the average retailer can now track their clientele from the grain of the decision-making process, all the way up to and even past the point of purchase, with no need for camouflage.

Leveraging online data in-store is difficult as consumer behaviour differs considerably between the two channels. It is, however, reckless to write off the in-store experience. Firms such as Argos are increasingly making the web more available in store as a way of getting more information, by either installing computer terminals or equipping staff with mobile devices. They are also exploiting the extensive research customers now do online before going into a store to make a purchase. Once intent is established online, this is cross-referenced at an aggregated level with actual purchases to establish correlations and refine segmentations. This information is then cross-referenced for a second time with transactional data from physical stores and the website to determine patterns between the two.

It is no longer simply enough to know everything about consumers. As they become increasingly aware of how much information is being gathered and how much it is being monetized, the issue of trust rears its head. Retail marketers must now not only harness Big Data to increase customer sentiment, they must also be aware of customer scepticism around sharing their personal detailers, and preventing negative effects of appearing too intrusive and forceful.

Customers cannot be made to feel like they are being exploited, the relationship must be reciprocal. Retail marketers looking to achieve this are increasingly looking to provide value-added services in order to make the consumer see that their interests are being considered. The marketer’s role is moving away from pumping them for cash, and instead moving towards using Big Data analytics to build relationships and improve customer sentiment this way.

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